CVE-2009-0657 in Face Recognition
Summary
by MITRE
Toshiba Face Recognition 2.0.2.32 allows physically proximate attackers to obtain notebook access by presenting a large number of images for which the viewpoint and lighting have been modified to match a stored image of the authorized notebook user.
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Analysis
by VulDB Data Team • 12/01/2017
This vulnerability affects Toshiba Face Recognition software version 2.0.2.32 and represents a significant security weakness in biometric authentication systems. The flaw resides in the face recognition algorithm's inability to properly validate the authenticity of presented images, creating a pathway for attackers to bypass user authentication through image manipulation techniques. The vulnerability is classified as a weakness in authentication mechanisms and falls under the broader category of biometric spoofing attacks that have been documented in cybersecurity literature for years. According to CWE standards, this represents a weakness in authentication through biometric recognition that allows for bypass of security controls through presentation attacks.
The technical implementation of this vulnerability stems from the software's reliance on visual matching algorithms that do not adequately account for image manipulation or presentation attacks. Attackers can exploit this by creating or obtaining multiple images of the target user, modifying these images to match lighting conditions and viewpoints of the stored biometric template, and then presenting these manipulated images to the face recognition system. The system's failure to detect these artificial presentations indicates a lack of anti-spoofing mechanisms and insufficient validation of image authenticity. This weakness specifically impacts the integrity of the authentication process by allowing unauthorized access through the presentation of modified images that match the stored user profile.
The operational impact of this vulnerability is substantial as it enables attackers with physical proximity to gain unauthorized access to notebook computers without requiring the legitimate user's credentials. This creates a serious risk for corporate and personal data security, as attackers can bypass the face recognition system through relatively simple image manipulation techniques. The vulnerability is particularly concerning because it does not require sophisticated technical skills or expensive equipment to exploit, making it accessible to a wide range of threat actors. The attack vector is classified as a physical proximity attack under the ATT&CK framework, specifically falling under the technique of credential access through presentation attacks that can be executed with minimal resources.
Mitigation strategies for this vulnerability should include implementing anti-spoofing mechanisms that detect manipulated or printed images, such as liveness detection features that verify the presence of a real person rather than a static image. Organizations should consider upgrading to newer versions of face recognition software that include proper anti-spoofing capabilities, and in some cases, implementing multi-factor authentication to reduce the risk of unauthorized access. The vulnerability highlights the importance of proper biometric system design that considers the full attack surface and implements robust validation mechanisms. Security practitioners should also consider the broader implications of biometric systems and ensure that any biometric authentication solution includes comprehensive testing for presentation attacks and proper validation of presented credentials.